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Location-Based Social Networks

Location-Based Social Networks. Chapter 8 and 9 of the book Computing with Spatial Trajectories. Yu Zheng and Xing Xie Microsoft Research Asia. Social Networks.

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Location-Based Social Networks

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  1. Location-Based Social Networks Chapter 8 and 9 of the book Computing with Spatial Trajectories Yu Zheng and Xing Xie Microsoft Research Asia

  2. Social Networks “A social network is a social structure made up of individuals connected by one or more specific types of interdependency, such as friendship, common interests, and shared knowledge.”

  3. Social Networking Services A social networking service builds on and reflects the real-life social networks among people through online platforms such as a website, providing ways for users to share ideas, activities, events, and interests over the Internet.

  4. Locations • Location-acquisition technologies • Outdoor: GPS, GSM, CDMA, … • Indoor: Wi-Fi, RFID, supersonic, … • Representation of locations • Absolute (latitude-longitude coordinates) • Relative (100 meters north of the Space Needle) • Symbolic (home, office, or shopping mall) • Forms of locations • Point locations • Regions • Trajectories

  5. Locations + Social Networks • Add a new dimension to social networks • Geo-tagged user-generated media: texts, photos, and videos, etc. • Recording location history of users • Location is a new object in the network • Bridging the gap between the virtual and physical worlds • Sharing real-world experiences online • Consume online information in the physical world

  6. Virtual world Examples Sharing & Understanding Interactions Physical world Generating & Consuming

  7. Location-Based Social Networks • Locations • An new dimension: Geo-tag • An new object • Social networks • Expanding social structures • Recommendations • Users • Locations • media • Sharing • Geo-tagged media • Virtual  Physical worlds • Understanding • User interests/preferences • Location property • User-user, location-location, user-location correlations Sharing Locations Understanding Social networks

  8. Location-Based Social Networks (LBSN) • not only mean adding a location to an existing social network so that people in the social structure can share location-embedded information, • but also consists of the new social structure made up of individuals connected by the interdependency derived from their locations in the physical world as well as their location-tagged media content • Here, the physical location consists of the instant location of an individual at a given timestamp and the location history that an individual has accumulated in a certain period. • The interdependency includes not only that two persons co-occur in the same physical location or share similar location histories • but also the knowledge, e.g., common interests, behavior, and activities, inferred from an individual’s location (history) and location-tagged data. From Book “Computing With Spatial Trajectories”

  9. Categories of LBSN Services Geo- • Geo-tagged-media-based • Point-location-driven • Trajectory-centric

  10. Mining User Similarity Based on Location History

  11. GIS ‘08/Trans. On the Web Grouping users in terms of the similarity between their location histories, and conduct personalized location recommendations.

  12. Mining User Similarity Based on Location History • Model user location history • Geographic spaces • Semantic spaces User similarity Semantic Location history Geo-Location history GPS trajectories

  13. Mining User Similarity Based on Location History • Computing user similarity • Dynamic programming

  14. 1. Stay point detection 2. Hierarchical clustering 3. Individual graph building

  15. Friend and Location Recommendation Similar Users Retrieval L1, L2, …., Ln u1 u2 . . un x1, x2, …, xn y1, y2, …, yn . . z1, z2, …, zn Ranking Locations Location Candidates Discovering User taste inferring

  16. Mining interesting locations and travel sequences from GPS trajectories

  17. Mining interesting locations, travel sequences, and travel experts from user-generated travel routes

  18. Users: Hub nodes The HITS-based inference model Locations: Authority nodes

  19. HITS (Hyperlink-Induced Topic Search) Algorithm Hubs (User) Authorities (Locations) L1 A=2 U1 H=3 L2 A=1 U2 H=1 L3 A=1

  20. HITS (Hyperlink-Induced Topic Search) Algorithm Hubs (User) Authorities (Locations) L1 A=5 U1 H=3 L2 A=3 U2 H=2 L3 A=3 Authority score: Sum of all hub scores of in-link nodes

  21. HITS (Hyperlink-Induced Topic Search) Algorithm Hubs (User) Authorities (Locations) L1 A=5 U1 H=8 L2 A=3 U2 H=5 L3 A=3 Hub score: Sum of all authority scores of out-link nodes

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